• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.139-144
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• 2017

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of IoT technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatial-temporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

• Database Research
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• v.34 no.3
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• pp.86-98
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• 2018

Point-of-Interest(POI) recommendation systems suggest the most interesting POIs to users considering the current location and time. With the rapid development of smartphones, internet-of-things, and location-based social networks, it has become feasible to accumulate huge amounts of user POI visits. Therefore, instant recommendation of interesting POIs at a given time is being widely recognized as important. To increase the performance of POI recommendation systems, several studies extracting users' POI sequential preference from POI check-in data, which is intended for implicit feedback, have been suggested. However, when constructing a model utilizing sequential preference, the model encounters possibility of data distortion because of a low number of observed check-ins which is attributed to intensified data sparsity. This paper suggests refinement of temporal intervals based on data confidence. When building a POI recommendation system using temporal intervals to model the POI sequential preference of users, our methodology reduces potential data distortion in the dataset and thus increases the performance of the recommendation system. We verify our model's effectiveness through the evaluation with the Foursquare and Gowalla dataset.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.119-124
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• 2023

This paper proposes a new reference model that can represent data access with temporal locality and popularity bias. Among existing reference models, the LRU-stack model can express temporal locality, which is a characteristic that the more recently referenced data has, the higher the probability of being referenced again. However, it cannot take into account differences in popularity of the data. Conversely, the independent reference model can reflect the different popularity of data, but has the limitation of not being able to model changes in data reference trends over time. The reference model presented in this paper overcomes the limitations of these two models and has the feature of reflecting both the popularity bias of data and their changes over time. This paper also examines the relationship between the cache replacement algorithm and the reference model, and shows the optimality of the proposed model.

• Journal of Korea Entertainment Industry Association
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• v.4 no.2
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• pp.58-63
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• 2010

The varieties of situated cognition applications provide various information to a user in a ubiquitous computing environment. In this paper, We propose a system that provides an optimized output using a fusion of temporal data and spatial data from sensing devices.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.60-68
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• 2012

Current wireless sensor networks are considered to support more complex operations ranging from military to health care which require energy-efficient and timely transmission of large amounts of data. In this paper, we propose an adaptive temporal suppression algorithm which exploits a temporal correlation among sensor readings. The proposed scheme can significantly reduce the number of transmitted sensor readings by sensor node, and consequently decrease the energy consumption and delay. Instead of transmitting all sensor readings from sensor node to sink node, the proposed scheme is to selectively transmit some elements of sensor readings using the adaptive temporal suppression, and the sink node is able to reconstruct the original data without deteriorating data quality by linear interpolation. In our proposed scheme, sensing data stream at sensor node is divided into many small sensing windows and the transmission ratio in each window is decided by the window complexity. It is defined as the number of a fluctuation point which has greater absolute gradient than threshold value. We have been able to achieve up about 90% communication reduction while maintaining a minimal distortion ratio 6.5% in 3 samples among 4 ones.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.4
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• pp.945-951
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• 2008

In this paper, a new high performance data cache scheme that improves exploitation of both the spatial and temporal locality is proposed. The proposed data cache consists of a hardware prefetch unit and two sub-caches such as a direct-mapped (DM) cache with a large block size and a fully associative buffer with a small block size. Spatial locality is exploited by fetching and storing large blocks into a direct mapped cache, and is enhanced by prefetching a neighboring block when a DM cache hit occurs. Temporal locality is exploited by storing small blocks from the DM cache in the fully associative buffer according to their activity in the DM cache when they are replaced. Experimental results on Spec2000 programs show that the proposed scheme can reduce the average miss ratio by $12.53%\sim23.62%$ and the AMAT by $14.67%\sim18.60%$ compared to the previous schemes such as direct mapped cache, 4-way set associative cache and SMI(selective mode intelligent) cache[8].

• Journal of KIISE
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• v.43 no.9
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• pp.963-973
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• 2016

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.153-160
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• 2009

Efficient buffer management is closely related to system performance. Thus, much research has been performed on various buffer management techniques. However, many of the proposed techniques utilize the temporal locality of access patterns. In spatial database environments, there exists not only the temporal locality but also spatial locality, where the objects in the recently accessed regions will be accessed again in the near future. Thus, in this paper, we present a buffer management technique, called BEAT, which utilizes both the temporal locality and spatial locality in spatial database environments. The experimental results with real-life and synthetic data demonstrate the efficiency of BEAT.

• Journal of KIISE
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• v.43 no.12
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• pp.1365-1375
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• 2016

One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.5
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• pp.553-563
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• 1999

The Spatial Database Engine(SDETM) developed by Environmental Systems Research Institute, Inc. is a spatial database that employs a client-server architecture incorporated with a set of software services to perform efficient spatial operations and to manage large, shared and geographic data sets. It currently supports a wide variety of spatial search methods and spatial relationships determined dynamically. Spatial objects in the space world can be changed by either non-spatial operations or spatial operations. Conventional geographical information systems(GISs) did not manage their historical information, however, because they handle the snapshot images of spatial objects in the world. In this paper we propose a spatio-temporal data model and an algorithm for temporal relationship macro which is able to manage and retrieve the historical information of spatial objects. The proposed spatio-temporal data model and its operations can be used as a software tool for history management of time-varying objects in database without any change.